Cancer Grade Research Articles

Breast cancer recurrence and tumor size: A bibliometric analysis

Abstract Background Breast cancer (BC) is the most common malignancy diagnosed in females. Over the years, many factors have been studied to understand what causes BC to come back. One of them is the tumor size at the beginning of the treatment. Tumor size is an interesting parameter, and some classifications of cancer grade depend on it. Methods This bibliometric analysis examined the pattern followed by publications on BC recurrence and tumor size. The Scopus database was used, and bibliometric analysis was utilized to identify the most prolific authors, the journals most involved in the field, and the countries, institutions, and keywords used. BC recurrence and tumor size have an exponential growth in the number of publications over the years. Results For the last 11 years, from 2012 to 2022, the trend of annual publications concerning BC recurrence and tumor size increased dramatically, showing that the majority of publications were published in the United States (452 articles) by Morrow, M., who authored 13 articles; The University of Texas, Anderson Cancer Center had the most published articles (48 articles); and Breast Cancer Research and Treatment journal achieved the largest quantity of published articles (112 articles). Conclusion Tumor size is an important risk factor for BC recurrence, and this bibliometric analysis focused on the high interest introduced by the research community.

Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms “in the Wild”

The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)–based algorithms employing deep learning have shown their ability to match pathologists’ performance in assigning Gleason scores, with the potential to enhance pathologists’ grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.

Two-tier architecture-based grading for pancreatic cancer outperforms traditional grading.

A novel architecture-based grading system for pancreatic ductal adenocarcinoma (PDAC) is tested against traditional grading. A total of 103 PDAC resections were graded by College of American Pathologists/American Joint Committee on Cancer (CAP/AJCC) guidelines and by a system using an architectural pattern (dispersed larger duct = low grade vs dense smaller duct = high grade). Survival analyses and interobserver variability were assessed. In total, 114 cases from a public data set were used for validation. Median overall survivals were 15 and 36 months for architectural high-grade and low-grade cases, respectively (P<.001). Conversely, CAP/AJCC grading showed no survival difference between well-differentiated and moderately differentiated tumors (P=.545). Architecture-based grading remained prognostically significant for recurrence-free survival (P=.004), but CAP/AJCC grading was not (P=.226). Adjusted for stage and margin status, architectural high-grade PDACs showed a hazard ratio of 2.69 relative to low grade (P<.001) for survival. The validation cohort confirmed prognostic differences in overall (P<.001) and recurrence-free survival (P=.027) for the architecture-based system, outperforming CAP/AJCC grading. Architecture-based grading exhibited a Cohen's ĸ value of 0.710 (substantial agreement), superior to traditional grading (0.488, moderate agreement). Grading PDAC based on architectural pattern results in superior prognostication and reproducibility vs CAP/AJCC grading.

External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size. The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa. The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85. The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

Robust, credible, and interpretable AI-based histopathological prostate cancer grading.

Prostate cancer (PCa) is among the most common cancers in men and its diagnosis requires the histopathological evaluation of biopsies by human experts. While several recent artificial intelligence-based (AI) approaches have reached human expert-level PCa grading, they often display significantly reduced performance on external datasets. This reduced performance can be caused by variations in sample preparation, for instance the staining protocol, section thickness, or scanner used. Another limiting factor of contemporary AI-based PCa grading is the prediction of ISUP grades, which leads to the perpetuation of human annotation errors. We developed the prostate cancer aggressiveness index (PCAI), an AI-based PCa detection and grading framework that is trained on objective patient outcome, rather than subjective ISUP grades. We designed PCAI as a clinical application, containing algorithmic modules that offer robustness to data variation, medical interpretability, and a measure of prediction confidence. To train and evaluate PCAI, we generated a multicentric, retrospective, observational trial consisting of six cohorts with 25,591 patients, 83,864 images, and 5 years of median follow-up from 5 different centers and 3 countries. This includes a high-variance dataset of 8,157 patients and 28,236 images with variations in sample thickness, staining protocol, and scanner, allowing for the systematic evaluation and optimization of model robustness to data variation. The performance of PCAI was assessed on three external test cohorts from two countries, comprising 2,255 patients and 9,437 images. Using our high-variance datasets, we show how differences in sample processing, particularly slide thickness and staining time, significantly reduce the performance of AI-based PCa grading by up to 6.2 percentage points in the concordance index (C-index). We show how a select set of algorithmic improvements, including domain adversarial training, conferred robustness to data variation, interpretability, and a measure of credibility to PCAI. These changes lead to significant prediction improvement across two biopsy cohorts and one TMA cohort, systematically exceeding expert ISUP grading in C-index and AUROC by up to 22 percentage points. Data variation poses serious risks for AI-based histopathological PCa grading, even when models are trained on large datasets. Algorithmic improvements for model robustness, interpretability, credibility, and training on high-variance data as well as outcome-based severity prediction gives rise to robust models with above ISUP-level PCa grading performance.

Prediction of tumor regression grade in far-advanced gastric cancer after preoperative immuno-chemotherapy using dual-energy CT-derived extracellular volume fraction.

This study examines the effectiveness of dual-energy CT (DECT) delayed-phase extracellular volume (ECV) fraction in predicting tumor regression grade (TRG) in far-advanced gastric cancer (FAGC) patients receiving preoperative immuno-chemotherapy. A retrospective analysis was performed on far-advanced gastric adenocarcinoma patients treated with preoperative immuno-chemotherapy at our institution from August 2019 to March 2023. Patients were categorized based on their TRG into pathological complete response (pCR) and non-pCR groups. ECV was determined using the delayed-phase iodine maps. In addition, tumor iodine densities and standardized iodine ratios were meticulously analyzed using the triple-phase enhanced iodine maps. Univariate analysis with five-fold cross-validation and Spearman correlation determined DECT parameters and clinical indicators association with pCR. The predictive accuracy of these parameters for pCR was evaluated using a weighted logistic regression model with five-fold cross-validation. Of the 88 patients enrolled (mean age 60.8 ± 11.1 years, 63 males), 21 (23.9%) achieved pCR. Univariate analysis indicated ECV's significant role in differentiating between pCR and non-pCR groups (average p value = 0.021). In the logistic regression model, ECV independently predicted pCR with an average odds ratio of 0.911 (95% confidence interval, 0.798-0.994). The model, incorporating ECV, tumor area, and IDAV (the relative change rate of iodine density from venous phase to arterial phase), showed an average area under curves (AUCs) of 0.780 (0.770-0.791) and 0.766 (0.731-0.800) for the training and validation sets, respectively, in predicting pCR. DECT-derived ECV fraction is a valuable predictor of TRG in FAGC patients undergoing preoperative immuno-chemotherapy. This study demonstrates that DECT-derived extracellular volume fraction is a reliable predictor for pathological complete response in far-advanced gastric cancer patients receiving preoperative immuno-chemotherapy, offering a noninvasive tool for identifying potential treatment beneficiaries.

Clinical Utility of Tumor-Naïve Presurgical Circulating Tumor DNA Detection in Early-Stage NSCLC

ObjectivesThe use of tumor-informed circulating tumor DNA (ctDNA) testing in patients with early-stage disease before surgery is limited, mainly owing to restricted tissue access and extended turnaround times. This study aimed to evaluate the clinical value of a tumor-naïve, methylation-based cell-free DNA assay in a large cohort of patients with resected NSCLC. MethodWe analyzed presurgical plasma samples from 895 patients with EGFR and anaplastic lymphoma kinase-wild-type, clinical stage I or II NSCLC. The ctDNA status was evaluated for its prognostic significance in relation to tumor volume, metabolic activity, histologic diagnosis, histologic subtypes, and clinical-to-pathologic TNM upstaging. ResultsPresurgical ctDNA detection was observed in 55 of 414 patients (13%) with clinical stage I lung adenocarcinoma (LUAD) and was associated with poor recurrence-free survival (2-year recurrence-free survival 69% versus 91%; log-rank p < 0.001), approaching that of clinical stage II LUAD. Presurgical ctDNA detection was not prognostic in patients with clinical stage II LUAD or non-LUAD. Within LUAD, tumor volume and positron emission tomography avidity interacted to predict presurgical ctDNA detection. Moreover, presurgical ctDNA detection was predictive of the postsurgical discovery of International Association for the Study of Lung Cancer grade 3 tumors (p < 0.001) and pathologic TNM upstaging (p < 0.001). Notably, presurgical ctDNA detection strongly correlated with higher programmed death-ligand 1 expression in tumors (positive rates 28% versus 55%, p < 0.001), identifying a subgroup likely to benefit from anti–programmed death-ligand 1 therapies. ConclusionThese findings support the integration of ctDNA testing into routine diagnostic workflows in early-stage NSCLC without the need for tumor tissue profiling. Furthermore, it is clinically useful in identifying patients at high risk who might benefit from innovative treatments, including neoadjuvant immune checkpoint inhibitors.

Radical prostatectomy cancer grade and percentage of Gleason pattern 4 estimated by global vs individual tumor grading correlate differently with the risk of biochemical recurrence in Grade Group 2 and 3 cancers.

There are 2 grading approaches to radical prostatectomy (RP) in multifocal cancer: Grade Group (GG) and percentage of Gleason pattern 4 (GP4%). We investigated whether RP GG and GP4% generated by global vs individual tumor grading correlate differently with biochemical recurrence. We reviewed 531 RP specimens with GG2 or GG3 cancer. Each tumor was scored separately with assessment of tumor volume and GP4%. Global grade and GP4% were assigned by combining Gleason pattern 3 and 4 volumes for all tumors. Correlation of GG and GP4% generated by 2 methods with biochemical recurrence was assessed by Cox proportional hazard regression and receiver operating characteristic curves, with optimism adjustment using a bootstrap analysis. Median age was 63 (range, 42-79) years. Median prostate-specific antigen was 6.3 (range, 0.3-62.9) ng/mL. In total, the highest-grade tumor in 371 (36.9%) men was GG2 and in 160 (30.1%) men was GG3. Global grading was downgraded from GG3 to GG2 in 37 of 121 (30.6%) specimens with multifocal disease, and 145 of 404 (35.9%) specimens had GP4% decreased by at least 10%. Ninety-eight men experienced biochemical recurrence within a median of 13 (range, 3-119) months. Men without biochemical recurrence were followed up for a median of 47 (range, 12-205) months. Grade Group, GP4%, and margin status correlated with the risk of biochemical recurrence using highest-grade tumor and global grading, but the degrees of these correlations varied and were statistically significantly different between the 2 grading approaches. Grade Group, GP4%, and margin status derived by global vs individual tumor grading predict postoperative biochemical recurrence statistically significantly differently. This difference has important implications if results derived from cohorts graded using different methods are compared.

385 Surgical and Oncological Anal Cancer Outcomes at a Regional Tertiary Centre

Abstract Aim There is little data in the UK about anal cancer outcomes. Our aim was to look at the rates of residual and recurrent disease following surgical or oncological treatment as well as the correspondence with the resection margin. Method A retrospective review of those who were diagnosed with anal cancer between 1/1/2017 and 1/1/2022. We reviewed their grade of cancer, the treatment received, the resection margin following surgery, and their outcomes. Results 95 patients were included. 84 patients received treatment with the aim of cure and 11 patients received palliation from diagnosis. Of those treated with an intent to cure, 61.9% of patients had no residual disease or disease recurrence, 17.9% of patients had residual disease and 20.2% had recurrence (10.7% local; 1.2% regional; 8.3% distant). 29.8% of this patient group have died. 54.1% of those with residual or local recurrence had salvage surgery. 30.8% had a positive margin. 100% of patients with a positive margin at salvage surgery have now died compared to 55.5% of those with a negative margin. 8 patients were treated with local excision for T1 disease. 62.5% had a R1 resection margin. Following further treatment 0% have had disease reoccurrence. Conclusions The survival rate of this group of patients is 62.1%. Despite having a negative resection margin at salvage surgery, long-term survival is poor. Compared with local excision of T1 disease, despite a positive or negative margin, survival is 100% in this group.

The Accuracy of Core Needle Biopsy in Determining Histological Type and Scarff- Bloom-Richardson Grade in Invasive Breast Cancer.

This 18-month retrospective study examined the accuracy of core needle biopsy (CNB) in diagnosing histological type and Scarff-Bloom-Richardson (SBR) grade in 110 invasive breast cancer patients at Rajshree Medical Research Institute, Bareilly. The study demonstrated 89.1% histological concordance between CNB and surgical specimens, with invasive ductal carcinoma identified with excellent accuracy. However, SBR grade concordance was moderate at 77.3%, suggesting CNB may not consistently reflect tumour aggressiveness. Statistics showed great sensitivity and specificity for histological typing, but CNB results should be interpreted cautiously, especially for grading and rare subtypes. CNB is still a useful diagnostic tool, although surgical examination may be needed to confirm and arrange treatment. Keywords: Core Needle Biopsy, Invasive Breast Cancer, Histological Type, SBR Grade

A prospective evaluation of the prostate microbiome in malignant and benign tissue using transperineal biopsy.

The link between the prostate microbiome and prostate cancer remains unclear. Few studies have analyzed the microbiota of prostate tissue, and these have been limited by potential contamination by transrectal biopsy. Transperineal prostate biopsy offers an alternative and avoids fecal cross-contamination. We aim to characterize the prostate microbiome using transperineal biopsy. Patients with clinical suspicion for prostate cancer who were to undergo transperineal prostate biopsy with magnetic resonance imaging(MRI) fusion guidance were prospectively enrolled from 2022 to 2023. Patients were excluded if they had Prostate Imaging Reporting and Data System lesions with scores ≤ 3, a history of prostate biopsy within 1 year, a history of prostate cancer, or antibiotic use within 30 days of biopsy. Tissue was collected from the MRI target lesions and nonneoplastic transitional zone. Bacteria were identified using 16S ribosomal RNA gene sequencing. Across the 42 patients, 76% were found to have prostate cancer. Beta diversity indices differed significantly between the perineum, voided urine, and prostate tissue. There were no beta diversity differences between cancerous or benign tissue, or between pre- and postbiopsy urines. There appear to be unique genera more abundant in cancerous versus benign tissue. There were no differences in alpha diversity indices relative to clinical findings including cancer status, grade, and risk group. We demonstrate a rigorous method to better characterize the prostate microbiome using transperineal biopsy and to limit contamination. These findings provide a framework for future large-scale studies of the microbiome of prostate cancer.

Expression of Intragenic LCAL4 Long Non-Coding RNAs as a Potential Diagnostic and Prognostic Marker in Female Breast Cancer.

In breast cancer early detection is associated with reduced mortality and it is essential to identify new biomarkers for early detection and appropriate management of cancer patients with the best response to treatment. Long non-coding RNAs (LncRNAs) have attracted much attention as potential diagnostic, prognostic, or predictive biomarkers due to their high specificity, easy access to non-invasive methods, and their aberrant expression under various pathological and physiological conditions. Have attracted the aim of this study is to investigate the expression profile of intragenic non-coding LncRNAs LCAL4 as a biomarker as potential diagnostic and prognostic biomarkers in cancer. In this research, 62 tissue samples were obtained from patients undergoing therapeutic surgery in Khatam al-Anbia Hospital and the normal peripheral tissue that was removed for prevention was used as a control by Real-time PCR method. The expression pattern of LCAL4 long non-coding RNA gene is significantly different between two groups of healthy control samples and samples obtained from patients with different breast cancer subtypes, Also its expression between samples obtained from different subgroups and different stages showed significant differences. The studied LncRNAs can act as a factor to identify tumor tissue from healthy tissue, and the diagnosis of cancer grades can be different depending on the type of LncRNA. These results can be proposed in the introduction of LncRNA LCAL4 as a new marker in the diagnosis of breast cancer. In addition, by interpreting the results, it can be concluded that these LncRNAs can be considered as influential factors in the process of breast cancer.

Prostate Cancer Among Black Men in Canada

Prostate cancer is a prevalent disease among men worldwide, exhibiting substantial heterogeneity in presentation and outcomes influenced by various factors, including race and ethnicity. Disparities in incidence, stage at diagnosis, and survival rates have been observed between Black men and those of other races and ethnicities. To compare prostate cancer outcomes between Black men and men with other race (Asian, Hispanic, Indigenous, Middle Eastern, White, Multiracial, and Other) in a universal health care system, with race and ethnicity self-reported. This was a prospective, observational cohort study of men diagnosed with prostate cancer between June 1, 2014, and August 28, 2023, who self-identified race and ethnicity. Participants included men who had been prospectively enrolled in the Alberta Prostate Cancer Research Initiative from the 2 major urology referral centers in Alberta (University of Alberta and University of Calgary). All men with prostate cancer enrolled in the initiative were included. Race and ethnicity. The primary outcome was the stage and grade of prostate cancer at diagnosis. Further outcomes included age and prostate-specific antigen level at diagnosis, initial treatment modality, time from diagnosis to initial treatment, and prostate cancer-specific, metastasis-free, and overall survivals. A total of 6534 men were included; 177 (2.7%) were Black, and 6357 (97.3%) had another race or ethnicity. Men who identified as Black were diagnosed with prostate cancer at an earlier age (mean [SD], 62.0 [8.2] compared with 64.6 [7.7] years; P < .001) and had a lower Charlson Comorbidity Index rating (14% compared with 7% ≤ 1; P < .001) compared with men of other races. Men who identified as Black had similar prostate-specific antigen levels at diagnosis, TNM category (74% vs 74% with T1-T2; P = .83) and Gleason Grade Group (34% compared with 35% Gleason Grade Group 1; P = .63). Black men had similar rates of prostate cancer-specific (hazard ratio [HR], 1.10; 95% CI, 0.41-2.97; P = .85), metastasis-free (HR, 0.88; 95% CI, 0.42-1.46; P = .44), and overall (HR, 0.55; 95% CI, 0.25-1.24; P = .15) survival. The findings of this cohort study suggest that Black men, despite being diagnosed at a younger age, experience comparable prostate cancer outcomes compared with men of other races.

Prognostic value of the international association for the study of lung cancer grading system and its association with the tumor microenvironment in stage I EGFR-muted lung adenocarcinoma

IntroductionThe International Association for the Study of Lung Cancer (IASLC) grading system predicts early lung adenocarcinoma outcomes. MethodsThe purpose of this study is to examine prognostic value of the IASLC grading system and its association with the tumor microenvironment (TME) in Stage I EGFR-muted lung adenocarcinoma. Based on the IASLC grading system, we compared the clinicopathological characteristics of EGFR-mutated lung adenocarcinoma (n = 296). In addition, we examined the expression level of E-cadherin in tumor cells and counted the number of tumor-infiltrating lymphocytes (TILs; CD8, CD20, CD138, and Foxp3), tumor-associated macrophages (TAMs; CD204), and cancer-associated fibroblasts (CAFs; podoplanin) using semi-automatic digital pathology image analysis. ResultsRecurrence-free survival (RFS) curve showed that survival of grade 3 was significantly shorter than that of grade 1 (P < 0.01) and grade 2 (P = 0.03). Multivariate analysis of RFS revealed the invasive size, lymphatic permeation, and grade 3 (P < 0.01) as independent poor prognostic factors. The number of CD204 +TAMs and PDPN+CAFs was significantly higher in grade 3 than in grade 1 or 2 (all P < 0.01). Among the intermediate grade by the predominant subtype based classification, cases classified as grade 3 by the new classification had higher number of CD204 +TAMs (P < 0.01) and PDPN+CAFs (P = 0.02) than those classified as grade 2. ConclusionThe IASLC grading system correlated with the outcomes of EGFR-mutated lung adenocarcinoma. Grade 3 was found to have the TME that most contributes to tumor progression, which probably explained their poor prognosis.

A semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning.

Pathological images are considered the gold standard for clinical diagnosis and cancer grading. Automatic segmentation of pathological images is a fundamental and crucial step in constructing powerful computer-aided diagnostic systems. Medical microscopic hyperspectral pathological images can provide additional spectral information, further distinguishing different chemical components of biological tissues, offering new insights for accurate segmentation of pathological images. However, hyperspectral pathological images have higher resolution and larger area, and their annotation requires more time and clinical experience. The lack of precise annotations limits the progress of research in pathological image segmentation. In this paper, we propose a novel semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning (MCL-Net), which combines consistency regularization methods with pseudo-labeling techniques. The MCL-Net architecture employs a shared encoder and multiple independent decoders. We introduce a Soft-Hard pseudo-label generation strategy in MCL-Net to generate pseudo-labels that are closer to real labels for pathological images. Furthermore, we propose a multi-consistency learning strategy, treating pseudo-labels generated by the Soft-Hard process as real labels, by promoting consistency between predictions of different decoders, enabling the model to learn more sample features. Extensive experiments in this paper demonstrate the effectiveness of the proposed method, providing new insights for the segmentation of microscopic hyperspectral tissue pathology images.

Specific microbiome patterns and their association with breast cancer: the intestinal microbiota as a potential biomarker and therapeutic strategy.

Breast cancer (BC) is one of the most diagnosed cancers in women. Based on histological characteristics, they are classified as non-invasive, or in situ (tumors located within the milk ducts or milk lobules) and invasive. BC may develop from in situ carcinomas over time. Determining prognosis and predicting response to treatment are essential tools to manage this disease and reduce its incidence and mortality, as well as to promote personalized therapy for patients. However, over half of the cases are not associated with known risk factors. In addition, some patients develop resistance to treatment and relapse. Therefore, it is necessary to identify new biomarkers and treatment strategies that improve existing therapies. In this regard, the role of the microbiome is being researched as it could play a role in carcinogenesis and the efficacy of BC therapies. This review aims to describe specific microbiome patterns associated with BC. For this, a literature search was carried out in PubMed database using the MeSH terms "Breast Neoplasms" and "Gastrointestinal Microbiome", including 29 publications. Most of the studies have focused on characterizing the gut or breast tissue microbiome of the patients. Likewise, studies in animal models and in vitro that investigated the impact of gut microbiota (GM) on BC treatments and the effects of the microbiome on tumor cells were included. Based on the results of the included articles, BC could be associated with an imbalance in the GM. This imbalance varied depending on molecular type, stage and grade of cancer, menopause, menarche, body mass index, and physical activity. However, a specific microbial profile could not be identified as a biomarker. On the other hand, some studies suggest that the GM may influence the efficacy of BC therapies. In addition, some microorganisms and bacterial metabolites could improve the effects of therapies or influence tumor development.

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset.

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care.

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.

To study the utility of COX-2 as immunohistochemical prognostic marker in comparison to various histopathological parameters and TNM staging in breast carcinoma: an observational, cross-sectional study protocol.

Breast cancer is the most prevalent cancer among women worldwide and is a well-known cause for cancer mortality in females. COX-2 (cyclooxygenase) plays a vital role in development of some human cancers such as lung, colon and breast. It is a potent enzyme that is important for the conversion of arachidonic acid into prostaglandins. These prostaglandins mediate cellular proliferation, apoptosis and angiogenesis which contributes to carcinogenesis. Overexpression of COX-2 has been detected in several malignancies including breast cancer. COX-2 overexpression is regarded as a poor prognostic marker of breast cancer.The present study will aim to study the immunohistochemical expression of COX-2 in breast cancer and compare it with known histopathological parameters thus assessing its prognostic value. This will be an observational study conducted in the Department of Pathology, JNMC, Wardha (Sawangi). Radical mastectomy specimens will be studied for COX-2 expression by immunohistochemistry in patients diagnosed with breast carcinoma. COX-2 expression will be quantified as immunohistochemical score and results will be correlated with various histopathological parameters. The expected result of our study will suggest an association of COX-2 expression to the factors associated with poor prognosis in breast carcinoma. A positive correlation is expected between larger tumor size, positive lymph node status, higher T stage and N stage and lymphovascular invasion. Conclusions will be drawn from the obtained results of the immunohistochemical study by using COX-2- for detection of overexpression of COX-2 when evaluated with TNM staging, histological grading and molecular types of breast cancer.

Prospective Evaluation of the Kaiser Permanente Prostate Cancer Risk Calculator in Biopsy-Naïve Men With Mild PSA Elevations.

Our goal was to assess the impact of providing prostate cancer risk estimates to patients and urologists among biopsy-naïve men with mild PSA elevations. This prospective intervention study was conducted in urology departments in Kaiser Permanente Northern California among biopsy-naïve men with mild PSA elevations (<10 ng/mL) between March 2021 and March 2023. The intervention was providing prostate cancer risk estimates for intermediate-/high-grade cancer (Grade Groups 2-5) and any cancer to patients and urologists using our previously validated risk calculator. Biopsy outcomes were compared to a historical comparison group biopsied between January 2016 and December 2019. Over 24 months, the risk calculator was used to estimate risk for 1962 men, of whom 1455 (74%) accepted the biopsy. The men who accepted the biopsy had higher predicted intermediate-/high-grade cancer risks (median 24%, interquartile range [IQR] 17%-31%, vs 20%, IQR 14%-29%; P < .0001) and higher predicted risks for any cancer (median 48%, IQR 40%-56%, vs 45%, IQR 37%-53%, respectively; P < .0001) than those who declined the biopsy. Compared to the historical group, use of the risk calculator resulted in a biopsy population enriched with intermediate-/high-grade cancer: 29% vs 25%; similar rates of low-grade cancer: 24% vs 24%; and fewer negative biopsies: 47% vs 51%, respectively; overall P = .013. In biopsy-naïve men with mild PSA elevations, use of this risk calculator resulted in a biopsy population that had more intermediate-/high-grade cancer and fewer negative biopsies compared to a historical group.